<?php
    require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');

    /**
     * PHPExcel_Linear_Best_Fit
     * Copyright (c) 2006 - 2015 PHPExcel
     * This library is free software; you can redistribute it and/or
     * modify it under the terms of the GNU Lesser General Public
     * License as published by the Free Software Foundation; either
     * version 2.1 of the License, or (at your option) any later version.
     * This library is distributed in the hope that it will be useful,
     * but WITHOUT ANY WARRANTY; without even the implied warranty of
     * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
     * Lesser General Public License for more details.
     * You should have received a copy of the GNU Lesser General Public
     * License along with this library; if not, write to the Free Software
     * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
     * @category   PHPExcel
     * @package    PHPExcel_Shared_Trend
     * @copyright  Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
     * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt    LGPL
     * @version    ##VERSION##, ##DATE##
     */
    class PHPExcel_Linear_Best_Fit extends PHPExcel_Best_Fit {
        /**
         * Algorithm type to use for best-fit
         * (Name of this trend class)
         * @var    string
         **/
        protected $bestFitType = 'linear';

        /**
         * Define the regression and calculate the goodness of fit for a set of X and Y data values
         * @param    float[] $yValues The set of Y-values for this regression
         * @param    float[] $xValues The set of X-values for this regression
         * @param    boolean $const
         */
        public function __construct($yValues, $xValues = [], $const = true) {
            if (parent::__construct($yValues, $xValues) !== false) {
                $this->linearRegression($yValues, $xValues, $const);
            }
        }

        /**
         * Execute the regression and calculate the goodness of fit for a set of X and Y data values
         * @param     float[] $yValues The set of Y-values for this regression
         * @param     float[] $xValues The set of X-values for this regression
         * @param     boolean $const
         */
        private function linearRegression($yValues, $xValues, $const) {
            $this->leastSquareFit($yValues, $xValues, $const);
        }

        /**
         * Return the Y-Value for a specified value of X
         * @param     float $xValue X-Value
         * @return     float                        Y-Value
         **/
        public function getValueOfYForX($xValue) {
            return $this->getIntersect() + $this->getSlope() * $xValue;
        }

        /**
         * Return the X-Value for a specified value of Y
         * @param     float $yValue Y-Value
         * @return     float                        X-Value
         **/
        public function getValueOfXForY($yValue) {
            return ($yValue - $this->getIntersect()) / $this->getSlope();
        }

        /**
         * Return the Equation of the best-fit line
         * @param     int $dp Number of places of decimal precision to display
         * @return     string
         **/
        public function getEquation($dp = 0) {
            $slope     = $this->getSlope($dp);
            $intersect = $this->getIntersect($dp);
            return 'Y = ' . $intersect . ' + ' . $slope . ' * X';
        }
    }
